{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "\n",
    "x = iris['data'][:,(2,3)]\n",
    "y = iris['target']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = (y == 0) |(y == 1)\n",
    "x=x[mask]\n",
    "y=y[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf = SVC(kernel='linear',  C=float('1.0'))\n",
    "svm_clf.fit(x,y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = svm_clf.coef_[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = svm_clf.intercept_[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_svm_bd(svm_clf,xmin,xmax,sv = True):\n",
    "    w = svm_clf.coef_[0]\n",
    "    b = svm_clf.intercept_[0]\n",
    "\n",
    "    x0 = np.linspace(xmin,xmax,200)\n",
    "    \n",
    "    line_bd = -w[0]/w[1]*x0 - b/w[1]\n",
    "    \n",
    "    margin = 1/w[1]\n",
    "\n",
    "    line_up = line_bd + margin\n",
    "\n",
    "    line_down = line_bd - margin\n",
    "\n",
    "    if sv:\n",
    "        svs = svm_clf.support_vectors_\n",
    "        plt.scatter(svs[:,0], svs[:,1], s = 180)\n",
    "    plt.plot(x0, line_bd,'k-')\n",
    "    plt.plot(x0, line_up,'k--')\n",
    "    plt.plot(x0, line_down,'k--')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x0 = np.linspace(0,5.5,200)\n",
    "pred_1 = 5 * x0 - 20\n",
    "pred_2 = x0 - 1.8\n",
    "pred_3 = 0.1 * x0 -20\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 5.5, 0.0, 2.0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,4))\n",
    "plt.subplot(121)\n",
    "\n",
    "plt.plot(x[:,0][y==1], x[:, 1][y==1], 'bs')\n",
    "plt.plot(x[:,0][y==0], x[:, 1][y==0], 'ys')\n",
    "\n",
    "plt.plot(x0, pred_2, 'm-', linewidth=2)\n",
    "plt.plot(x0, pred_3, 'r-', linewidth=2)\n",
    "\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.plot(x0, pred_2, 'm-', linewidth=2)\n",
    "plt.plot(x0, pred_3, 'r-', linewidth=2)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(x[:,0][y==1], x[:, 1][y==1], 'bs')\n",
    "plt.plot(x[:,0][y==0], x[:, 1][y==0], 'ys')\n",
    "plot_svm_bd(svm_clf, 0, 5.5)\n",
    "\n",
    "plt.axis([0, 5.5, 0, 2])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
